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Data-driven Smooth-to-hard Spectral Regularization for General Linear Inverse Problems
arXiv eess.IV0 views
arXiv:2610.21664v1 Announce Type: cross Abstract: The logistic smooth-to-hard shrinker is the proximal map of an explicit saturating spectral penalty for general linear inverse problems. Its exact weak convexity yields a unique scaled update and proximal-gradient stationarity guarantees, while independent validation provides finite-grid parameter-selection control. We use MRI as an illustrative example.
Source：https://arxiv.org/abs/2610.21664PreviousDenoising Textures is a Different Ballgame Altogether!NextPanoDiff-SR: Synthesizing Dental Panoramic Radiographs using Diffusion and Super-resolution
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